Advanced Bioprocessing Sensor and Analytical Technologies for Induced Pluripotent Stem Cell Culture Online Monitoring and Automation
This project aims to develop a robust online sensor to monitor the redox state; and build a knowledge graph hybrid model-based machine learning (KG-ML) algorithm to advance the scientific understanding of metabolism.
Large-scale production of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines, but iPSCs’ productivity and pluripotency are highly sensitive to culture conditions.
Subtle changes in culture conditions can lead to stress which can result in cell populations with heterogeneous differentiation potential.
Develop a robust online sensor to monitor the redox state
Build a knowledge graph hybrid model-based machine learning (KG-ML) algorithm to advance the scientific understanding of metabolism by correlating online sensing and the intracellular metabolic state of the culture process
This model-based monitoring and control will safeguard that the culture follows the expected trajectory for successful growth and expansion.
To achieve these goals and facilitate large-scale production of iPSCs, the team will conduct the following tasks:
Refine the two-photon excitation (TPE) fluorescence optical sensor to iPSC cultures for real-time measurement of the intracellular redox state
Gain knowledge of iPSC metabolism in static and bioreactor cultures with aggregates and on microcarriers
Extend the KG-ML framework to improve the prediction and control of iPSCs outcomes
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